Methods and systems for a content development and management platform

ABSTRACT

The present system and method relate to enhancements to online search engines and search result rankings, which can benefit from large scale analysis of online content (e.g., Web pages) and vast amounts of information kept and processed from prior searches to develop intelligent associations between various content with one another. Some aspects employ machine learning systems and methods to further enhance the present goals. Other aspects employ novel arrangements of data in data stores to extract best associations and deliver greater search engine rankings to users in an increasingly context-based or personalized type of searching environment.

CLAIM TO PRIORITY

This application is based upon, claims priority to, and incorporates byreference U.S. Provisional Patent Application No. 62/419,772, entitled“System and Method for Topical Machine Search Handling,” filed on Nov.9, 2016.

TECHNICAL FIELD

The present application relates to the fields of automated computersearching and ranking of search results, and to electronic data storesused in the same, including for generating outputs and data used todetermine online subject matter in a topic-driven and context-sensitivesearch environment.

BACKGROUND

Millions of users turn to the Internet through client browserapplications such as Firefox, Safari, Explorer, Chrome and others insearch of information and other content stored on the global network.Perhaps the most popular use of the Internet and the World Wide Web isthrough search engines that provide results in response to user searchesentered into the search engine interfaces. Each of the major browserproviders (and others) have developed widely used search engines thatreap significant economic returns to their providers. A search engine isusually presented to a user as a simple user entry field into which auser types a word, phrase, natural language question, or into which theuser enters an image file other search query. The search engines,typically within seconds, carry out proprietary and highly complexsearch algorithms to sort or rank a response to the user's query, whichis typically presented in the browser interface as a list of rankedsearch results. The lists may preferentially present paid content orsites in some search engines. In others, the search results arepresented in descending order of relevance. Search results are sometimesreferred to as “hits” and a typical search may return thousands of hitssorted by the search engine according to the data available to it andaccording to the results of the search engine's processing of said data.The Internet is a repository of such a large amount of data, of widelyvariable quality and value, such that a poorly designed search enginewould be of little or no use to its users. Poor search methods caninundate a user with irrelevant or useless responses to the user's queryand therefore bad search engines are quickly relegated to oblivion bynegative publicity and non-use. Search engines are recognized by theirability to quickly find the most relevant and useful search results touser queries. The best search engines, based on the quality, depth andspeed of their operation, are very highly used by all segments ofsociety, and as such command lucrative advertising and placement feesfor content such as banner advertising. In addition, search enginesgenerate enormous value through their ability to track and retainhistorical data generated in the course of billions of searchescontinuously taking place in said search engines. This ability makessearch engines very valuable for predictive analytics, marketing andpersonalization of services including targeted communications andadvertising at the mass and individual levels. In particular, marketershave used Internet searching and search engine functionality to deliverfavorable content to potential consumers. Marketers or promoters havebeen known to generate content relating to a subject of interest (amarket segment, product line, idea being promoted) and to then generatecorresponding Web and social media content directed to an aspect of thesubject of interest so as to drive up interest in the subject ofinterest. In other words, secondary conversations, postings or contentdirected to a sub-space of the subject of interest can mutually drive upsearch engine rankings of both the sub-space and the subject ofinterest, to the benefit of the marketers or their clients.

Enterprises increasingly rely on their online presence for success,including content websites (including mobile sites), mobileapplications, social networks, and electronic commerce sites. As theseforms of online presence grow across a wide range of enterprises, aconsistent challenge for each enterprise has been how to be discoveredby relevant parties seeking engagement with the enterprise or its onlineresources. Search engines are still the dominant solution for most usersseeking content; accordingly, in addition to bidding for presence in the“sponsored” portion of search results, enterprises have historicallysought to optimize their content to increase the likelihood of beingpresented at or near the top of the search results for relevantsearches. Major search engines, like Google™, have historically usedranking algorithms that ascribe importance to relevant keywordsoccurring in content and also ascribe weight to the linking of others tothe content; therefore, content publishers and advertisers havetypically sought to include relevant keywords in the content, metadata,advertisements, and the like that comprise their online presence andhave sought to encourage linking to the content. The prevalence ofkeyword-based approaches has limited their effectiveness, as manyparties have sought to load their content with the same keywords, oftenin situations where the keywords do not bear a close relationship to theactual content of a site. In response, search engines have begun to seekalternatives to keywords in ranking content, and some search engineshave, in an effort to discourage misleading use of keywords, ceased toexpose their ranking methodologies, making it increasingly difficult forenterprises to manage activities that promote relevant discovery. A needexists for methods and systems that enable enterprises to developcontent for their online presence that is highly relevant, withoutdependence on conventional keyword-driven approaches.

With many metrics available and varying, obscure techniques for rankingwithin search engines, it is difficult for marketer and other workersfor an enterprise to know (a) around what topics content should becreated and (b) whether a selected content approach is truly performingwell. At every stage of the game, it is difficult to know what contentis or will be effective. One problem contributing to this confusion isthe fact that many marketers choose to focus on certain keywords aroundwhich to craft content around. Historically, marketers could strive tohave their content rank highly (e.g., on search engines like Google™,etc.), and use search engine optimization (SEO) techniques to make theirpages as appealing as possible within the ranking procedure of thesearch engine (e.g., based on the engine's attributing value to a page'sauthority, relevance, etc.). This approach has several challenges.First, it is often hard to select which keywords to target. Also,ranking highly for a term might not necessarily correlate to attractingmore customers. Further, search engines are moving to a more“personalized” search results mode based on a searcher's location,history, and the like that makes the concept of “rank” an inherentlytroublesome metric on which to base efforts for success.

A number of different enterprise functions, and individuals involved invarious roles within those functions, depend on identifying content forcommunications, including marketing functions, sales functions, servicesfunctions, public relations functions, investor relations functions, andothers. For example, a marketing professional inside an enterprise or anagency working for an enterprise may need to determine favorable topicsfor website content or for an advertising campaign, while a salesprofessional may wish to engage with a customer and wonder what topicwould be best to encourage engagement. Customers increasingly expectmore personalized engagement with an enterprise, reflected by theemergence of chat functions, conversational agents, and bots, all ofwhich tune communications more closely to the situation of a specificindividual. As the number of such conversations increases rapidly inscale, it is increasingly challenging to meet such needs solely by humanworkers. For marketers who don't know what to write about, currentsolutions involve extremely metrics-heavy keyword research tools tochoose these keywords. For marketers who can't evaluate their content'sperformance, current solutions involve looking at the content's rank,which is rapidly becoming less and less relevant, and perhaps using ananalytics tool to map traffic. Neither of these solutions help guidemarketers on how to fix potential problems, and both solutions can evengive an incorrect perspective on content's success. A need exists forautomated methods and systems that assist various functions and roleswithin an enterprise in finding appropriate topics to draw customersinto relevant conversations and to extend the conversations in a waythat is relevant to the enterprise and to each customer.

A proliferation of metrics is used to rank potential search results by asearch engine according to the art. Successful promoters (whetherpromoting products, ideas or other notions) are constantly looking forways to improve the positive exposure of their subjects of interest,leveraging the search engine and ranking metrics. However, theover-abundance of options and strategies for generating search resultsand sorting the same makes it challenging for promoters to determinewhat topics and related sub-topics of interest to offer so as tomaximize their overall desired impact. Also, it is complicated anddifficult for such promoters to determine whether their content is infact having a positive impact on the promoted subject, e.g., marketingcampaigns, political campaigns, etc. An entire industry catering to“search engine optimization” (SEO) has emerged, which purports tooptimize a customer's online efforts for maximum search engine yield.Some of the promoters' challenges are caused by their focus on the useof key words in their promotional campaigns and online promotionalcontent. But it is far from a known process how to best select theoptimum sets of key words to achieve maximum search result ranking withthe various major search engines. These challenges are complicatedrecently by the move to “personalize” search results whereby majorsearch engines are not limiting their rankings to objective or absolutemetrics, but instead, a personalized and context-based ranking based onnumerous subjective factors are being employed. Therefore, there may notbe an ideal or known optimized methodology by which to carry out SEO asthe field develops and becomes more sophisticated.

SUMMARY

The present disclosure is directed to various ways of improving thefunctioning of computer systems, information networks, data stores,search engine systems and methods, and other advantages. As statedabove, more personalized, context-dependent search engine operations aredeparting from traditional absolute metrics for ranking search results.Instead, search results may be further dependent on the identity,demographic, location and online history of a person making a searchquery. More sophisticated search engine systems and methods are requiredto give an advantage to a promoter or marketer in such a searchenvironment so as to push the marketer or promoter's subject matter tothe top of the search engine rankings.

Among other features and advantages, the present system and method canbenefit from large scale analysis of online content (e.g., Web pages)and vast amounts of information kept and processed from prior searchesto develop intelligent associations between various content with oneanother. Some aspects employ machine learning systems and methods tofurther enhance the present goals including assisting users to developsuccessful content strategy for online content generation. Other aspectsemploy novel arrangements of data in data stores to extract bestassociations and deliver greater search engine rankings to users in anincreasingly context-based or personalized type of searchingenvironment. In particular, this system and method can improve thearchitecture that guides promoters to understanding what online subjectmatter to direct their efforts at for maximum effect on the targetmarketplace.

In embodiments of the present disclosure, a platform is provided forenabling automated development of content, typically for an enterprise,that is adapted to support a variety of enterprise functions, includingmarketing strategy and communications, website development, searchengine optimization, sales force management, electronic commerce, socialnetworking, and others. Among other benefits, the content developmentplatform uses a range of automated processes to extract and analyzeexisting online content of an enterprise, parse and analyze the content,and develop a cluster of additional content that is highly relevant tothe enterprise, without reliance on conventional keyword-basedtechniques.

In embodiments, the platform integrates functions of a contentmanagement system (CMS) with functions of a customer relationshipmanagement (CRM) system, including sharing access to database recordsand other information that is typically stored in and/or accessed byeither.

In embodiments, methods and systems are provided herein for a platformfor generating a cluster of correlated content from a primary onlinecontent object, the methods and systems including an automated crawlerfor crawling the primary online content object and storing a set ofresults from the crawling in a data storage facility; a parser forparsing the stored content from the crawling to generate a plurality ofkey phrases and to generate a content corpus from the primary onlinecontent object; a plurality of models for processing at least one of thekey phrases and the corpus, the models comprising at least two of aword2vec model, a doc2vec model, a latent semantic analysis (LSA)extraction model, and a key phrase logistic regression model, whereinthe processing results in a plurality of content clusters representingtopics within the primary online content object; a content cluster datastore for storing the content clusters; and a suggestion generator forgenerating, using output from at least one of the models, a suggestedtopic that is similar to at least one topic among the content clustersand for storing the suggested topic and information regarding thesimilarity of the suggested topic to at least one content cluster in thecontent cluster data store.

In embodiments, the plurality of models used by the platform maycomprise at least one of a word2vec model, a doc2vec model, a latentsemantic analysis extraction model, a latent semantic indexing model, aprinciple component analysis model, and a key phrase logistic regressionmodel. In embodiments, the parser uses a machine learning system toparse the crawled content. In embodiments, the machine learning systemiteratively applies a set of weights to input data, wherein the weightsare adjusted based on a parameter of success, wherein the parameter ofsuccess is based on the success of suggested topics in the onlinepresence of an enterprise. In embodiments, the machine learning systemis provided with a training data set that is created based on humananalysis of the crawled content.

In embodiments, at least one of the plurality of models used in theplatform uses a machine learning system to cluster content. Inembodiments, the machine learning system iteratively applies a set ofweights to input data, wherein the weights are adjusted based on aparameter of success, wherein the parameter of success is based on thesuccess of suggested topics in the online presence of an enterprise. Inembodiments, the machine learning system is provided with a trainingdata set that is created based on human clustering of a set of contenttopics.

In embodiments, the suggestion generator uses machine learning tosuggest topics. In embodiments, the machine learning system iterativelyapplies a set of weights to input data, wherein the weights are adjustedbased on a parameter of success, wherein the parameter of success isbased on the success of suggested topics in the online presence of anenterprise. In embodiments, the machine learning system is provided witha training data set that is created based on human creation of a set ofsuggested topics.

In embodiments, the methods and systems disclosed herein may furtherinclude an application for developing a strategy for development ofonline presence content, the application accessing the content clusterdata store and having a set of tools for exploring and selectingsuggested topics for online presence content generation. In embodiments,the application provides a list of topics that are of highest semanticrelevance for an enterprise based on the parsing of the primary onlinecontent object. In embodiments, the methods and systems may furtherinclude a user interface of the application for presenting a suggestion,wherein the generated suggestion is presented with an indicator of thesimilarity of the suggested topic to a content cluster topic ascalculated by at least one of the models. In embodiments, the methodsand systems may further include a user interface of the application forpresenting a suggested topic, wherein the user interface facilitatesgeneration of content related to the suggested topic. In embodiments,the user interface includes at least one of key words and key phrasesthat represent the suggested topic. In embodiments, the at least one ofkey words and key phrases are used to prompt the user with content forgeneration of online presence content. In embodiments, the onlinepresence content is at least one of website content, mobile applicationcontent, a social media post, a customer chat, a frequently askedquestion item, a product description, a service description and amarketing message. In embodiments, the user interface for generation ofcontent includes a plurality of suggested topics, each associated withan indicator of the similarity of a given suggested topic to a contentcluster topic as calculated by at least one of the models.

In embodiments, the data storage facility is a cloud-based storagefacility. In embodiments, the data storage facility is a distributeddata storage facility.

In embodiments, the primary online content is a web page of anenterprise. In embodiments, the primary online content is a social mediapage of an enterprise.

In embodiments, the methods and systems may further include anapplication for developing a strategy for development of online presencecontent, the application accessing the content cluster data store andhaving a set of tools for exploring and selecting suggested topics foronline presence content generation, wherein the application furtheraccesses the content of a customer relationship management system. Inembodiments, the application includes a user interface for developingcontent regarding a suggested topic for presentation in a communicationto a customer, wherein selection of a suggested topic for presentationto a customer is based at least in part on a semantic relationshipbetween the suggested topic as determined by at least one of the modelsand at least one data record relating to the customer stored in thecustomer relationship management system.

Also provided herein are methods and systems for automated discovery oftopics for interactions with customers of an enterprise, includingmethods and systems that assist various functions and roles within anenterprise in finding appropriate topics to draw customers into relevantconversations and to extend the conversations in a way that is relevantto the enterprise and to each customer. Automated discovery of relevantcontent topics may support processes and workflows that require insightinto what topics should be written about, such as during conversationswith customers. Such processes and workflows may include development ofcontent by human workers, as well as automated generation of content,such as within automated conversational agents, bots, and the like.Automated discovery may include identifying concepts that are related byusing a combination of analysis of a relevant item of text (such as corecontent of a website, or the content of an ongoing conversation) with ananalysis of linking (such as linking of related content). Inembodiments, this may be performed with awareness at a broad scale ofthe nature of content on the Internet, such that new, related topics canbe automatically discovered that further differentiate an enterprise,while remaining relevant to its primary content. The new topics can beused within a wide range of enterprise functions, such as marketing,sales, services, public relations, investor relations and otherfunctions, including functions that involve the entire lifecycle of theengagement of a customer with an enterprise.

These and other systems, methods, objects, features, and advantages ofthe present disclosure will be apparent to those skilled in the art fromthe following detailed description of the preferred embodiment and thedrawings.

All documents mentioned herein are hereby incorporated in their entiretyby reference. References to items in the singular should be understoodto include items in the plural, and vice versa, unless explicitly statedotherwise or clear from the text. Grammatical conjunctions are intendedto express any and all disjunctive and conjunctive combinations ofconjoined clauses, sentences, words, and the like, unless otherwisestated or clear from the context.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure and the following detailed description of certainembodiments thereof may be understood by reference to the followingfigures:

FIG. 1 depicts a high-level flow in which a content platform is used toprocess online content, identify a cluster of semantically relevanttopics and produce generated online presence content involving thesemantically relevant topics.

FIG. 2 provides a functional block diagram of certain components andelements of a content development platform, including elements forextracting key phrases from a primary online content object, a contentcluster data store for storing clusters of topics and a contentdevelopment and management application having a user interface fordeveloping content.

FIGS. 3, 4, and 5 show examples of user interface elements forpresenting suggested topics and related information.

FIG. 6 provides a functional block diagram of certain components andelements of a content development platform, including integration of acustomer relationship management system with other elements of theplatform.

FIG. 7 provides a detailed functional block diagram of components andelements of a content development platform.

FIG. 8 illustrates a user interface for reporting information relatingto online content generated using the content development and managementplatform.

FIG. 9 depicts a user interface in which activity resulting from the useof the platform is reported to a marketer or other user.

DETAILED DESCRIPTION

Embodiments of the present disclosure are directed to computers,computer systems, networks and data storage arrangements comprisingdigitally encoded information and machine-readable instructions. Thesystems are configured and arranged so as to accomplish the presentmethods, including by transforming given inputs according to saidinstructions to yield new and useful outputs determining behaviors andphysical outcomes. Users of the present system and method will gain newand commercially significant abilities to convey ideas and to promote,create, sell and control articles of manufacture, goods, and otherproducts. The machinery in which the present system and method areimplemented will therefore comprise novel and useful devices andarchitectures of computing and processing equipment for achieving thepresent objectives.

With reference to FIG. 1, in embodiments of the present disclosure, aplatform is provided having a variety of methods, systems, components,services, interfaces, processes, components, data structures and otherelements (collectively referred to as the “content development platform100” except where context indicates otherwise), which enable automateddevelopment, deployment and management of content, typically for anenterprise, that is adapted to support a variety of enterprisefunctions, including marketing strategy and communications, websitedevelopment, search engine optimization, sales force management,electronic commerce, social networking, and others. Among otherbenefits, the content development platform 100 uses a range of automatedprocesses to extract and analyze existing online content of anenterprise, parse and analyze the content, and develop a cluster ofadditional content that is highly relevant to the enterprise, withoutreliance on conventional keyword-based techniques. Referring to FIG. 1,the content development platform 100 may generally facilitate processingof a primary online content object 102, such as a main web page of anenterprise, to establish a topic cluster 168 of topics that are relevantto one or more core topics 106 that are found in or closely related tothe content of the primary line content object 102, such as based onsemantic similarity of the topics in the topic cluster 168, includingcore topics 106, to content within the primary content object 102. Theplatform 100 may further enable generation of generated online presencecontent 160, such as reflecting various topics in the topic cluster 168,for use by marketers, sales people, and other writers or contentcreators on behalf of the enterprise.

In embodiments, the content development platform 100 includes methodsand systems for generating a cluster of correlated content from theprimary online content object 102. In embodiments, the primary onlinecontent object 102 is a web page of an enterprise. In embodiments, theprimary online content object 102 is a social media page of anenterprise. In the embodiments described throughout this disclosure, themain web page of an enterprise, or of a business unit of an enterprise,is provided as an example of a primary online content object 102 and insome cases herein is described as a “pillar” of content, reflecting thatthe web page is an important driver of business for the enterprise, suchas for delivering marketing messages, managing public relations,attracting talent, and routing or orienting customers to relevantproducts and other information. References to a web page or the likeherein should be understood to apply to other types of primary onlinecontent objects 102, except where context indicates otherwise. Anobjective of the content development platform 100 may be to drivetraffic to a targeted web page, in particular by increasing thelikelihood that the web page may be found in search engines, or by usersfollowing links to the web page that may be contained in other content,such as content developed using the content development platform 100.

In an aspect, the present systems, data configuration architectures andmethods allow an improvement over conventional online content generationschemes. As stated before, traditional online promotional content reliedon key word placement and on sympathetic authorship of a main subject(e.g., a web site) and corresponding secondary publications (e.g., blogsand sub-topical content related to the web site), which methods rely onknown objective and absolute ranking criteria to successfully promoteand rank the web site and sub-topical content. In an increasinglysubjective, personalized and context-sensitive search environment, thepresent systems and methods develop canonical value around a primaryonline content object such as a web site. In an aspect, a cluster ofsupportive and correlated content is intelligently generated orindicated so as to optimize and promote the online work product of apromoter (e.g., in support of an agenda or marketing effort). In anexample, large numbers of online pages are taken as inputs to thepresent system and method (e.g., using a crawling, parallel orsequential page processing machine and software).

As shown in simplified FIG. 1, a “core topic” 106 or main subject for apromotional or marketing effort, related to one or more topics, phrases,or the like extracted based on the methods and systems described hereinfrom a primary online content object 102, may be linked to a pluralityof supporting and related other topics, such as sub-topics. The coretopic 106 may comprise, for example, a canonical source of informationon that general subject matter, and preferably be a subject supportingor justifying links with other information on the general topic of aprimary online content object 102. In embodiments, visitors to a sitewhere generated online content 160 is located can start at a hyperlinkedsub-topic of content and be directed to a core topic 106 within a page,such as a page linked to a primary online content object 102 or to theprimary online content object 102 itself. In an example, a core topic106 can be linked to several (e.g., three to eight, or more) sub-topics.A recommendation or suggestion tool, to be described further below, canrecommend or suggest sub-topics, or conversely, it can dissuade orsuggest avoidance of sub-topics based on automated logic, which can beenabled by a machine learned process. As will be discussed herein, acontent strategy may be employed in developing the overall family oflinked content, and the content strategy may supersede conventional keyword based strategies according to some or all embodiments hereof.

In embodiments, the system and method analyze, store and processinformation available from a crawling step, including for a givenpromoter's web site (e.g., one having a plurality of online pages) so asto determine a salient subject matter and potential sub-topics relatedto said subject matter of the site. Associations derived from thisprocessing and analysis are stored and further used in subsequentmachine learning based analyses of other sites. Data derived from theanalysis and storage of the above pages, content and extracted analyticsmay be organized in an electronic data store, which is preferably alarge aggregated database and which may be organized for example usingMYSQL or a similar format.

FIG. 2 provides a detailed functional block diagram of certaincomponents and elements of a content development platform, includingelements for extracting key phrases from a primary online contentobject, a content cluster data store for storing clusters of topics anda content development and management application 150 having a userinterface for developing content. Within the platform 100, key phrases112 are extracted from the primary online content object 102 and areprocessed, such as using a variety of models 118, resulting in one ormore content clusters 130 that are stored in a content cluster datastore 132. The clusters may comprise the topic clusters 168 that aresemantically relevant to core topics reflected in the primary contentobject 102, as indicated by the key phrases. The models 118, which mayaccess a corpus of content extracted by crawling a relevant set of pageson the Internet, are applied to the key phrases 112 to establish theclusters, which arrange topics around a core topic based on semanticsimilarity. From the content clusters 130 a suggestion generator 134 maygenerate one or more suggested topics 138, which may be presented in auser interface 152 of a content development management application 150within which an agent of an enterprise, such as a marketer, a salesperson, or the like may view the suggested topic 138 and relevantinformation about it (such as indicators of its similarity or relevancyas described elsewhere herein) and create content, such as web pages,emails, customer chats, and other online presence content 160 on behalfof the enterprise. Within the interface 152, the resulting generatedonline presence content 160 may be linked to the primary online contentobject 102, such that the primary online content object 102 and one ormore generated online presence objects 160 form a cluster ofsemantically related content, such that visitors to any one of theobjects 102, 160 may be driven, including by the links, to the otherobjects 102, 160. In particular, the platform 100 enables drivingviewers who are interested in the topics that differentiate theenterprise to the online presence content, such as the main web pages,of the enterprise. Performance of the topics may be tracked, such as ina reporting and analytics system 180, such that performance-basedsuggestions may be provided by the suggestion generator 134, such as bysuggesting more suggested topics 138 that are similar to ones that havedriven increases in traffic to the primary online content object 102.

The system and method are then capable of projection of the crawled,stored and processed information, using the present processing hardware,networking and computing infrastructure so as to generatespecially-formatted vectors, e.g., a single vector. The vector orvectors can be according to a Word2vec model used to produce wordembeddings in a multi-layer neural network or similar arrangement. Thoseskilled in the art may appreciate that further reconstruction oflinguistic contexts of words are possible by taking a body of content(e.g., language words) to generate such vector(s) in a suitable vectorspace. Said vectors may further indicate useful associations of wordsand topical information based on their proximity to one another in saidvector space. Vectors based on other content information (e.g., phrasesor documents, which can be referred to as Phrase2vec or Document2vecherein) may also be employed in some embodiments. Documents or pageshaving similar semantic meaning would be conceptually proximal to oneanother according to the present model. In this way, new terms orphrases or documents may be compared against known data in the datastore of the system and generate a similarity, relevance or nearnessquantitative metric. Cosine similarity or other methods can be employedas part of this nearness determination. The similarity may be translatedinto a corresponding score in some embodiments. In other aspects, saidscore may be used as an input to another process or another optionalpart of the present system. In yet other aspects, the output may bepresented in a user interface presented to a human or machine. The scorecan further be presented as a “relevance” metric. Human-readablesuggestions may be automatically generated by the system and method andprovided as outputs, output data or output signals in a processor-drivenenvironment such as a modern computing architecture. The suggestions mayin some aspects provide a content context model for guiding promoters(e.g., marketers) towards a best choice of topical content to prepareand put up on their web sites, including suitable and relevantrecommendations for work product such as articles and blog posts andsocial media materials that would promote the promoters' main topics orsubjects of interest or sell the products and services of the marketersusing the system and method.

In an aspect, the present system and method allows for effectiverecommendations to promoters that improve the link structure betweenexisting content materials such as online pages, articles and posts. Inanother aspect, this allows for better targeting of efforts of apromoter based on the desired audience of the efforts, including largegroups, small groups or even individuals.

Implementations of the present system and method can vary as would beappreciated by those skilled in the art. For example, the system andmethod can be used to create a content strategy tool using processinghardware and special machine-readable instructions executing thereon.Consider as a simple illustrative example that a promoter desires tobest market a fitness product, service or informational topic. This canbe considered as a primary or “core topic” about which other secondarytopics can be generated, which are in turn coupled to or related to thecore topic. For example, weight lifting, dieting, exercise or othersecondary topics may be determined to have a favorable context-basedrelevance to the core topic. Specific secondary sub-topics about weightlifting routines, entitled, e.g., ‘Best weight lifting routines for men’or ‘How to improve your training form’ (and so on) may be each turnedinto a blog post that links back to the core topic web page.

When a user uses the content strategy tool of the present system andmethod the user may be in some embodiments prompted to select or enter acore (primary) topic based on the user's own knowledge or the user'sfield of business. The tool may them use this, along with a large amountof crawled online content that was analyzed, or along with extractedinformation resulting from such crawling of online content and priorstored search criteria and results, which is now context-based, tovalidate a topic against various criteria.

In an example, topics are suggested (or entered topics are rated) basedon the topics' competitiveness, popularity and relevance. Those skilledin the art may appreciate other similar criteria which can be used asmetrics in the suggestion or evaluation of a topic.

Competitiveness can comprise a measure of how likely a domain (Webdomain) would be ranked on “Page 1” for a particular term or phrase. Thelower the percentile ranking, the more difficult it is to rank for thatterm or phrase (e.g., as determined by a Moz rank indicating a site'sauthority).

Popularity as a metric is a general measure of a term or phrase'speriodic (e.g., monthly) search volume from various major searchengines. The greater this percentage, the more popular the term orphrase is.

Relevance as a metric generally indicates how close a term or phrase isto other content put up on the user's site or domain. The lower therelevance, the further away the term or phrase is from what the coretopic of the site or domain is. This can be automatically determined bya crawler that crawls the site or domain to determine its main or coretopic of interest to consumers. If relevance is offered as a service bythe present system and method a score can be presented through a user ormachine interface indicating how relevant the new input text is to anexisting content pool.

Timeliness of the content is another aspect that could be used to drivecontent suggestions or ratings with respect to a core topic. Forexample, a recent-ness (recency) metric may be used in addition to thosegiven above for the sake of illustration of embodiments of the systemand method.

Therefore, analysis and presentation of information indicating crossrelationships between topics becomes effective under the present scheme.These principles may further be applied to email marketing orpromotional campaigns to aid in decision making as to the content ofemails sent to respective recipients so as to maximally engage therecipients in the given promotion.

Other possible features include question classification; documentretrieval; passage retrieval; answer processing; and factoid questionanswering.

Note that the present concepts can be carried across languages insofaras an aspect hereof provides for manual or automated translation from afirst language to a second language, and that inputs, results andoutputs of the system can be processed in one or another language, or ina plurality of languages as desired.

FIG. 3, FIG. 4, and FIG. 5 are illustrative depictions of exemplarysimplified aspects of the present system, method and tools. Thesedepictions are not meant to be exhaustive or limiting, but are merelyexamples of how some features could be provided to a user of the systemand method.

Some embodiments hereof employ a latent semantic analysis (LSA) model,encoded using data in a data store and programmed instructions and/orprocessing circuitry to generate an output comprising an associationbetween various content by the promoter user of the system and method.LSA being applied here to analyze relationships between a (large) set ofdocuments and the data contained therein. In one embodiment machinelearning may be used to develop said association output or outputs.

FIG. 6 provides a functional block diagram of certain additionaloptional components and elements of the content development platform100, including integration of a customer relationship management system158 with other elements of the platform. In embodiments, the generatedonline content object 160 may comprise messaging content for a customerinteraction that is managed via a customer relationship managementsystem 158. In embodiments, the customer relationship management system158 may include one or more customer data records 164, such asreflecting data on groups of customers or individual customers,including demographic data, geographic data, psychographic data, datarelating to one or more transactions, data indicating topics of interestto the customers, data relating to conversations between agents of theenterprise and the customers, data indicating past purchases, interestin particular products, brands, or categories, and other customerrelationship data. The customer data records 164 may be used by theplatform 100 to provide additional suggested topics 138, to select amongsuggested topics 138, to modify suggested topics 138, or the like. Inembodiments, the CRM system 158 may support interactions with acustomer, such as through a customer chat 184, which in embodiments maybe edited in the user interface 152 of the content development andmanagement application 150, such as to allow a writer, such as an insidesales person or marketer who is engaging in the customer chat 184 withthe customer to see suggested topics 138 that may be of interest to thecustomer, such as based on the customer data records 164 and based onrelevancy of the topics to the main differentiators of the enterprise.In embodiments, a conversational agent 182 may be provided within orintegrated with the platform 100, such as for automating one or moreconversations between the enterprise and a customer. The conversationalagent 182 may take suggested topics from the suggestion generator 134 tofacilitate initiation of conversations with customers around topics thatdifferentiate the enterprise, such as topics that are semanticallyrelevant to key phrases found in the primary online content object 102.In embodiments, the conversational agent 182 may populate a customerchat 184 in the user interface 152, such as providing seed or draftcontent that a writer for the enterprise can edit.

FIG. 7 provides a detailed functional block diagram of components andelements of a content development platform. The methods and systems mayinclude an automated crawler 104 for crawling the primary online contentobject 102 and storing a set of results from the crawling in a datastorage facility 108. In embodiments, the data storage facility is acloud-based storage facility, such as a simple storage facility, such asan S3™ bucket provided by Amazon™, such as on a web service platform,such as the Amazon Web Services™ (AWS) platform. In embodiments, thedata storage facility is a distributed data storage facility. Inembodiments, the automated crawler 104 crawls one or more domainsassociated with an enterprise customers' content, such as the customer'sportal, main web page, or the like, as the primary online content object102, in order to identify topics already in use on those sites andstores the pages in S3™ storage, with metadata in a database, such as aMySQL database. The content development platform 100 may include aparser 110 for parsing the stored content from the crawling activity ofthe automated crawler 104 to generate a plurality of key phrases 112 andto generate a content corpus 114 from the primary online content object102. The content development platform 100 may include, use or integratewith one or more of a plurality of models 118 for processing at leastone of the key phrases 112 and the corpus 114.

The models 118 may include one or more of a word2vec model 120, adoc2vec model 122, a latent semantic analysis (LSA) extraction model, orLSA model 124, and a key phrase logistic regression model 128, whereinthe processing results in a plurality of content clusters 130representing topics within the primary online content object 102. Inembodiments, the platform 100 may take content for a primary contentobject 102, such as a website, and extract a number of phrases, such asa number of co-located phrases, based on processing the n-grams presentin the content (e.g., unigrams, bi-grams, tri-grams, tetra-grams, and soon), which may in the LSA model 124, be ranked based on the extent ofpresence in the content and based on a vocabulary that is more broadlyused across a more general body of content, such as a broad set ofInternet content. This provides a vector representation of a websitewithin the LSA model 124. Based on crawling with automatic crawler 104of over 619 million pages on the public internet (seeking to ignoreignoring those pages that are light on content), an LSA model 124 hasbeen trained using machine learning, using a training set of more than250 million pages, such that the LSA model 124 is trained to understandassociations between elements of content.

In embodiments, the one or more models 118 include the word2vec model120 or other model (e.g., doc2vec 122 or phrase2vec) that projectscrawled-domain primary online object content 102, such as fromcustomers' domains, into a single vector. In embodiments, the vectorspace is such that documents that contain similar semantic meaning areclose together. The application of the word2vec model 120 and thedoc2vec model 122 to the vector representation of primary content object102 (e.g., website) to draw vectors may result in a content-contextmodel based on co-located phrases. This allows new terms to be comparedagainst that content context database to determine how near it is to theenterprise's existing primary online content objects 102 (e.g.,webpages), such as using cosine similarity. That similarity may then beconverted into a score and displayed through the UI, such as displayingit as a “Relevancy” score. Ultimately, the content context model may beused to give recommendations and guidance for how individuals can choosegood topics to write about, improve the link structure of existingcontent, and target marketing and other efforts based on theiraudiences' individual topic groups of interest. In embodiments, theplurality of models 118 used by the platform may comprise other forms ofmodel for clustering documents and other content based on similarity,such as a latent semantic indexing model, a principle component analysismodel, or the like. In embodiments other similar models may be used,such as a phrase2vec model, or the like.

An objective of the various models 118 is to enable clustering ofcontent, or “topic clusters 168” around relevant key phrases, where thetopic clusters 168 include semantically similar words and phrases(rather than simply linking content elements that share exactly matchingkeywords). Semantic similarity can be determined by calculating vectorsimilarity around key phrases appearing in two elements of content. Inembodiments, topic clusters may be automatically clustered, such as byan auto-clustering engine 172 that manages a set of software jobs thattake web pages from the primary content object 102, use a model 118,such as the LSA model 124 to turn the primary content object 102 into avector representation, project the vector representation on to a space(e.g., a two-dimensional space), perform an affinity propagation thatseeks to find natural groupings among the vectors (representing clustersof ideas within the content), and show the groupings as clusters ofcontent. Once groups are created, a reviewer, such as a marketer orother content developer, can select one or more “centers” within theclusters, such as recognizing a core topic within the marketer's“pillar” content (such as a main web page), which may correspond to theprimary content object 102. Nodes in the cluster that are in closeproximity to the identified centers may represent good additional topicsabout which to develop content or to which to establish links; forexample, topic clusters can suggest an appropriate link structure amongcontent objects managed by an enterprise and with external contentobjects, such as third-party objects, where the link structure is basedon building an understanding of a semantic organization of cluster oftopics and mirroring the other content and architecture of linkssurrounding a primary content object 102 based on the semanticorganization.

The content development platform 100 may include a content cluster datastore 132 for storing the content clusters 130. The content cluster datastore 132 may comprise a MySQL database or other type of database. Thecontent cluster data store 132 may store mathematical relationships,based on the various models 118, between content objects, such as theprimary content object 102 and various other content objects or topics,which, among other things, may be used to determine what pages should bein the same cluster of pages (and accordingly should be linked to eachother). In embodiments, clusters are based on matching semantics betweenphrases, not just matching exact phrases. Thus, new topics can bediscovered by observing topics or subtopics within semantically similarcontent objects in a cluster that are not already covered in a primarycontent object 102. In embodiments, an auto-discovery engine 170 mayprocess a set of topics in a cluster to automatically discoveradditional topics that may be of relevance to parties interested in thecontent of the primary content object 102.

In embodiments, topics within a cluster in the content cluster datastore 132 may be associated with a relevancy score 174 (built from themodels 118), which in embodiments may be normalized to a single numberthat represents the calculated extent of semantic similarity of adifferent topic to the core topic (e.g., the center of a cluster, suchas reflecting the core topic of a primary content object 102, such as amain web page of an enterprise). The relevancy score 174 may be used tofacilitate recommendations or suggestions about additional topics withina cluster that may be relevant for content development.

The content development platform may include a suggestion generator 134for generating, using output from at least one of the models, asuggested topic 138 that is similar to at least one topic among thecontent clusters and for storing the suggested topic 138 and informationregarding the similarity of the suggested topic 138 to at least onecontent cluster 130 in the content cluster data store 132. Suggestedtopics 138 may include sub-topic suggestions, suggestions for additionalcore topics and the like, each based on semantic similarity (such asusing a relevancy score 174 or similar calculation) to content in theprimary content object 102, such as content identified as being at thecenter of a cluster of topics. Suggestions may be generated by using thekeyphrase logistic regression model 128 on the primary content object102, which, among other things, determines, for a given phrase that issimilar to the content in a cluster, how relatively unique the phrase isrelative to a wider body of content, such as all of the websites thathave been crawled across the broader Internet. Thus, through acombination of identifying semantically similar topics in a cluster(e.g., using the word2vec model 120, doc2vec model 122, and LSA model124) and identifying which of those are relatively differentiated (usingthe keyphrase logistic regression model 128), a set of highly relevant,well differentiated topics may be generated, which the suggestiongenerator 134 may process for production of one or more suggested topics138.

In embodiments, the parser 110 uses a parsing machine learning system140 to parse the crawled content. In embodiments, the machine learningsystem 140 iteratively applies a set of weights to input data, whereinthe weights are adjusted based on a parameter of success, wherein theparameter of success is based on the success of suggested topics 138 inthe online presence of an enterprise. In embodiments, the machinelearning system is provided with a parser training data set 142 that iscreated based on human analysis of the crawled content.

In embodiments, at least one of the plurality of models used in theplatform uses a clustering machine learning system 144 to clustercontent into the content clusters 130. In embodiments, the clusteringmachine learning system 144 iteratively applies a set of weights toinput data, wherein the weights are adjusted based on a parameter ofsuccess, wherein the parameter of success is based on the success ofsuggested topics in the online presence of an enterprise. Inembodiments, the machine learning system is provided with a trainingdata set that is created based on human clustering of a set of contenttopics.

In embodiments, the suggestion generator 134 uses a suggestion machinelearning system 148 to suggest topics. In embodiments, the suggestionmachine learning system 148 iteratively applies a set of weights toinput data, wherein the weights are adjusted based on a parameter ofsuccess, wherein the parameter of success is based on the success ofsuggested topics in the online presence of an enterprise. Inembodiments, the suggestion machine learning system 148 is provided witha training data set that is created based on human creation of a set ofsuggested topics.

In embodiments, the methods and systems disclosed herein may furtherinclude a content development and management application 150 fordeveloping a strategy for development of online presence content, theapplication 150 accessing the content cluster data store 132 and havinga set of tools for exploring and selecting suggested topics 138 foronline presence content generation. In embodiments, the application 150provides a list of suggested topics 138 that are of highest semanticrelevance for an enterprise based on the parsing of the primary onlinecontent object. In embodiments, the methods and systems may furtherinclude a user interface 152 of the application 150 for presenting asuggestion, wherein the generated suggestion is presented with anindicator of the similarity 154 of the suggested topic 138 to a topic inthe content cluster 130 as calculated by at least one of the models 118.

In embodiments, the content development and management application 150may include a cluster user interface 178 portion of the user interface152 in which, after a primary content object 102 has been brought onboard to the content development platform 100, a cluster of linkedtopics can be observed, including core topics in the primary contentobject 102 and various related topics. The cluster user interface 178may allow a user, such as a sales or marketing professional, to explorea set of topics, such as seeing topics that are highly relevant to abrand of the enterprise and related topics, which, in embodiments, maybe presented with a relevancy score 174 or other measure of similarity,as well as with other information, such as search volume information andthe like. In embodiments, the cluster user interface 178 or otherportion of the user interface 152 may allow a user to select and attachone or more topics or content objects, such as indicating which topicsshould be considered at the core for the enterprise, for a brand, or fora particular project. Thus, the cluster framework embodied in thecluster user interface 178 allows a party to frame the context of whattopics the enterprise wishes to be known for online (such as for theenterprise as a whole or for a brand of the enterprise).

The content development and management application 150 may comprise acontent strategy tool that encourages users to structure content inclusters based on the notion that topics are increasingly more relevantthat keywords, so that enterprises should focus on owning a contenttopic, rather than going after individual keywords. Each topic cluster168 may have a “core topic,” such as implemented as a web page on thatcore topic. For example, on a personal trainer's website, the core topicmight be “weightlifting.” Around those core topics 106 should besubtopics (in this example, this might include things like “bestweightlifting routines” or “how to improve your weightlifting form”),each of which should be made into a blog post that links back to thecore topic page.

When users use the content development and management application 150,or content strategy tool, the user may be prompted to enter a topicbased on the user's own knowledge of the enterprise. The contentdevelopment and management application 150 or tool may also useinformation gleaned by crawling domains of the enterprise with theautomated crawler 104, such as to identify existing topic clusters ontheir site (i.e., the primary online content object 102). For eachidentified core topic, the topic may be validated based on one or moremetrics or criteria, such as competitiveness, popularity, relevancy, orthe like, such as reflected by relevancy based on cosine similaritybetween a topic and the core topic, or based on various other sources ofwebsite analytics data. Competitiveness may comprise a measure of howlikely a domain or primary online content object 102 is to rank highly,such as on a first page of search engine results, for a particular word,phrase, or term. The lower the percentage on this metric, the harder itwill be to achieve a high rank for that term. This may be determined bya source like MozRank™ (provided by Moz™), a PageRank™ (provided byGoogle™), or other ranking metric, reflecting the primary online contentobject's 102 domain authority, absent other factors. Popularity maycomprise a general measure of a topic's monthly search volume or similaractivity level, such as from various search engines. The higher thepercentage, the more popular the term. This may be obtained from asource like SEMRush™, such as with data in broad ranges of 1-1000,1000-10000, etc. Relevancy may comprise a metric of close a topic,phrase, term or the like to other content, such as topic already coveredin other domains of a user, or the like. The lower the relevancy, thefurther away a given term is from what an enterprise is known for, suchas based on comparison to a crawl by the automated crawler 104 of theenterprise's website and other domains. Relevancy may be provided orsupported by the content context models 118 as noted throughout thisdisclosure.

As the models 118 analyze more topics, the models learn and improve,such that increasingly accurate measures may be provided as relevancyand the like. Once the user has selected a topic, the user may beprompted to identify subtopics related to that topic. Also, the platform100 may recommend or auto-fill subtopics that have been validated basedon their similarity to the core topic and based on other scoringmetrics. When the user has filled out a cluster of topics, the platform100 may alert the user to suggested links connecting each subtopic pageto a topic page, including recommending adding links where they arecurrently absent. The content development and management application 150may also allow customers to track the performance of each cluster,including reporting on various metrics used by customers to analyzeindividual page performance. The content development and managementapplication 150 or tool may thus provide several major improvements overour current tools, including a better “information architecture” tounderstand the relationship between pieces of content, built-in keywordvalidation, and holistic analysis of how each cluster of topicsperforms.

In embodiments, the user interface 152 facilitates generation ofgenerated online presence content 160 related to the suggested topic138. In embodiments, the user interface 152 includes at least one of keywords and key phrases that represent the suggested topic 138, which maybe used to prompt the user with content for generation of onlinepresence content. In embodiments, the generated online presence contentis at least one of website content, mobile application content, a socialmedia post, a customer chat, a frequently asked question item, a productdescription, a service description and a marketing message. Inembodiments, the generated online presence content may be linked to theprimary content object 102, such as to facilitate traffic between thegenerated online presence content and the primary content object 102 andto facilitate discovery of the primary content object 102 and thegenerated online presence content 160 by search engines 162. The userinterface 152 for generating content may include a function forexploring phrases for potential inclusion in generated online presencecontent 160; for example, a user may input a phrase, and the platform100 may use a relevancy score 174 or other calculation to indicate adegree of similarity. For example, if a topic is only 58% similar to acore topic, then a user might wish to find something more similar. Userinterface elements, such as colors, icons, animated elements and thelike may help orient a user to favorable topics and help avoidunfavorable topics.

In embodiments, the application 150 may facilitate creation and editingof content, such as blog posts, chats, conversations, messages, websitecontent, and the like, and the platform may parse the phrases written inthe content to provide a relevancy score 174 as the content is written.For example, as a blog is being written, the marketer may see whetherphrases that are being written are more or less relevant to a primarycontent object 102 that has been selected and attached to an enterprise,a project, or a brand within the platform 100. Thus, the contentdevelopment and management application 150 may steer the content creatortoward more relevant topics, and phrases that represent those topics.This may include prompts and suggestions from the suggestion generator134. The user interface 152 may include elements for assisting the userto optimize content, such as optimizing for a given reading level andthe like. The user interface 152 may provide feedback, such asconfirming that the right key phrases are contained in a post, so thatit is ready to be posted.

In embodiments, the application 150 for developing a strategy fordevelopment of generated online presence content 160 may access contentcluster data store 132 and may include various tools for exploring andselecting suggested topics 138 for generating the generated onlinepresence content 160. In embodiments 150, the application 150 mayfurther access the content of the customer relationship management (CRM)system 158. In embodiments, the application 150 includes a userinterface 152 for developing content regarding a suggested topic 138 forpresentation in a communication to a customer, wherein selection of asuggested topic 138 for presentation to a customer is based at least inpart on a semantic relationship between the suggested topic asdetermined by at least one of the models 118 and at least one customerdata record 164 relating to the customer stored in the customerrelationship management system 158.

The platform 100 may include, be integrated with, or feed a reportingand analytics system 180 that may provide, such as in a dashboard orother user interface, such as, in a non-limiting example, in the userinterface 152 of the content development and management application 150,various reports and analytics 188, such as various measures ofperformance of the platform 100 and of the generated online contentobject 160 produced using the platform 100, such as prompted bysuggestions of topics. As search engines have increasingly obscuredinformation about how sites and other content objects are ranked (suchas by declining to provide keywords), it has become very important todevelop alternative measures of engagement. In embodiments, the platform100 may track interactions across the life cycle of engagement of anenterprise with a customer, such as during an initial phase ofattracting interest, such as through marketing or advertising that maylead to a visit to a website or other primary online content object 102,during a process of lead generation, during conversations or engagementwith the customer (such as by chat functions, conversational agents, orthe like), during the process of identifying relevant needs and productsthat may meet those needs, during the delivery or fulfillment of ordersand the provision of related services, and during any post-salefollow-up, including to initiate further interactions. By integrationwith the CRM system 158 of an enterprise, the platform 100 may providemeasures that indicate what other activities of or relating tocustomers, such as generation of leads, visits to web pages, traffic andclickstream data relating to activity on a web page, links to content,e-commerce and other revenue generated from a page, and the like, wererelated to a topic, such as a topic for which a generated online contentobject 160 was created based on a suggestion generated in the platform100. Thus, by integration of a content development and managementapplication 150 and a CRM system 158, revenue can be linked to generatedcontent 160 and presented in the reporting and analytics system 180.

FIG. 8 shows an example of a user interface of the reporting andanalytics system 180.

In general, a wide range of analytics may be aggregated by topic cluster(such as a core topic and related topics linked to the core topic in thecluster), rather than by web page, so that activities involved ingenerating the content in the cluster can be attributed with the revenueand other benefits that are generated as a result. Among these areelements tracked in a CRM system 158, such as contact events, customers(such as prospective customers, leads, actual customers, and the like),deals, revenue, profit, and tasks.

In embodiments, the platform 100 may proactively recommend core topics,such as based on crawling and scraping existing site content of anenterprise. Thus, also provided herein is the auto-discovery engine 170,including various methods, systems, components, modules, services,processes, applications, interfaces and other elements for automateddiscovery of topics for interactions with customers of an enterprise,including methods and systems that assist various functions and roleswithin an enterprise in finding appropriate topics to draw customersinto relevant conversations and to extend the conversations in a waythat is relevant to the enterprise and to each customer. Automateddiscovery of relevant content topics may support processes and workflowsthat require insight into what topics should be written about, such asduring conversations with customers. Such processes and workflows mayinclude development of content by human workers, as well as automatedgeneration of content, such as within automated conversational agents,bots, and the like. Automated discovery may include identifying conceptsthat are related by using a combination of analysis of a relevant itemof text (such as core content of a website, or the content of an ongoingconversation) with an analysis of linking (such as linking of relatedcontent). In embodiments, this may be performed with awareness at abroad scale of the nature of content on the Internet, such that new,related topics can be automatically discovered that furtherdifferentiate an enterprise, while remaining relevant to its primarycontent. The new topics can be used within a wide range of enterprisefunctions, such as marketing, sales, services, public relations,investor relations and other functions, including functions that involvethe entire lifecycle of the engagement of a customer with an enterprise.

As noted above, customers increasingly expect more personalizedinteractions with enterprises, such as via context-relevant chats thatproperly reflect the history of a customer's relationship with theenterprise. Chats, whether undertaken by human workers, or increasinglyby intelligent conversational agents, are involved across all of thecustomer-facing activities of an enterprise, including marketing, sales,public relations, services, and others. Content development and strategyis relevant to all of those activities, and effective conversationalcontent, such as managed in a chat or by a conversational agent 182,needs to relate to relevant topics while also reflecting informationabout the customer, such as demographic, psychographic and geographicinformation, as well as information about past interactions with theenterprise. Thus, integration of the content development and managementplatform 100 with the CRM system 158 may produce appropriate topicswithin the historical context of the customer and the customer'sengagement with the enterprise. For example, in embodiments, tickets ortasks may be opened in a CRM system 158, such as prompting creation ofcontent, such as based on customer-relevant suggestions, via the contentdevelopment and management application 150, such as content for aconversation or chat with a customer (including one that may be managedby a conversational agent 182 or bot), content for a marketing messageor offer to the customer, content to drive customer interest in a webpage, or the like. In embodiments, a customer conversation or customerchat 184 may be managed through the content development and managementapplication 150, such as by having the chat occur within the userinterface 152, such that an agent of the enterprise, like an insidesales person, can engage in the chat by writing content, while seeingsuggested topics 138, indicators of relevance or similarity 154 and thelike. In this context, relevance indicators can be based on scores notedabove (such as reflecting the extent of relevance to core topics thatdifferentiate the enterprise), as well as topics that are of interest tothe customer, such as determined by processing information, such as onhistorical conversations, transactions, or the like, stored in the CRMsystem 158. In embodiments, to facilitate increased, the customer chat184 may be populated with seed or draft content created by an automatedconversational agent 182, so that a human agent can edit the contentinto a final version for the customer interaction.

In embodiments, the models 118 (collectively referred to as one or morecontent context models), and the platform 100 more generally, may enablea number of capabilities and benefits, including helping users come upwith ideas of new topics to write about based on a combination of thecontent cluster data store 132, a graph of topics for the site or othercontent of the enterprise, and one or more analytics. This may helpwriters find gaps in content that should be effective, but that are notcurrently written about. The models 118, and platform 100 may alsoenable users to come up with ideas about new articles, white papers andother content based on effective topics. The models 118, and platform100 may also enable users to understand effectiveness of content at thetopic level, so that a user can understand which topics are engagingpeople and which aren't. This may be analyzed for trends over time, so auser can see if a topic is getting more or less engagement. The models118, and platform 100 may also enable users to apply information abouttopics to at the level of the individual contact record, such as in thecustomer relationship management system 158, to help users understandwith what content a specific person engages. For example, for a user“Joe,” the platform 100, by combining content development and managementwith customer relationship management, may understand whether Joe isengaging more in “cardio exercise” or “weight lifting.” Rather than onlylooking at the aggregate level, user may at the individual level forrelevant topics. Development of content targeted to an individual'stopics of interest may be time-based, such as understanding what contenthas recently been engaged with and whether preferences are changing overtime.

The models 118, and platform 100 may also enable looking at crossrelationships between topics. For example, analytics within the platform100 and on engagement of content generated using the platform 100 mayindicate that people who engage frequently with a “cardio” topic alsoengage frequently with a “running” topic. If so, the platform 100 mayoffer suggested topics that are interesting to a specific person basedon identifying interest in one topic and inferring interest in others.

The models 118, and platform 100 may also enable development of emailcontent, such as based on understanding the topic of the content of anemail, an email campaign, or the like. This may include understandingwhich users are engaging with which content, and using that informationto determine which emails, or which elements of content within emails,are most likely to be engaging to specific users.

FIG. 8 illustrates a user interface for reporting information relatingto online content generated using the content development and managementplatform. Various indicators of success, as noted throughout thisdisclosure, may be presented, such as generated by the reporting andanalytics systems 180.

FIG. 9 depicts an embodiment of a user interface in which activityresulting from the use of the platform is reported to a marketer orother user. Among other metrics that are described herein, the userinterface can report on what customers, such as ones to be entered intoor already tracked in the CRM system, have had a first session ofengagement with content, such as a web page, as a result of the contentstrategy, such as where the customers arrive via a link contained in asub-topic or other topic linked to a core topic as described herein.

The present concepts can be applied to modern sophisticated searchingmethods and systems with improved success. For example, in acontext-sensitive or personalized search request, the results may beinfluenced by one or more of the following: location, time of day,format of query, device type from which the request is made, andcontextual cues.

In an embodiment, a topical cluster comprising a core topic and severalsub-topics can be defined and refined using the following generalizedprocess: 1. Mapping out of several (e.g., five to ten) of the topicsthat a target person (e.g., customer) is interested in; 2. Group thetopics into one or more generalized (core) topic into which thesub-topics could be fit; 3. Build out each of the core topics withcorresponding sub-topics using keywords or other methods; 4. Map outcontent ideas that align with each of the core topics and correspondingsub-topics; 5. Validate each idea with industry and competitiveresearch; and 6. Create, measure and refine the data and models andcontent discovered from the above process. These steps are not intendedto be limiting or exhaustive, as those skilled in the art mightappreciate alternate or additional steps suiting a given application.Some of the above steps may also be omitted or combined into one step,again, to suit a given application at hand.

In some embodiments, a system and method are provided that can be usedto provide relevancy scores (or quantitative metrics) as a service.Content generation suggestions can also be offered as a service usingthe present system and method, including synonyms, long tail key wordsand enrichment by visitor analytics in some instances.

Having thus described several aspects and embodiments of the technologyof this application, it is to be appreciated that various alterations,modifications, and improvements will readily occur to those of ordinaryskill in the art. Such alterations, modifications, and improvements areintended to be within the spirit and scope of the technology describedin the application. For example, those of ordinary skill in the art willreadily envision a variety of other means and/or structures forperforming the function and/or obtaining the results and/or one or moreof the advantages described herein, and each of such variations and/ormodifications is deemed to be within the scope of the embodimentsdescribed herein.

Those skilled in the art will recognize, or be able to ascertain usingno more than routine experimentation, many equivalents to the specificembodiments described herein. It is, therefore, to be understood thatthe foregoing embodiments are presented by way of example only and that,within the scope of the appended claims and equivalents thereto,inventive embodiments may be practiced otherwise than as specificallydescribed. In addition, any combination of two or more features,systems, articles, materials, kits, and/or methods described herein, ifsuch features, systems, articles, materials, kits, and/or methods arenot mutually inconsistent, is included within the scope of the presentdisclosure.

The above-described embodiments may be implemented in any of numerousways. One or more aspects and embodiments of the present applicationinvolving the performance of processes or methods may utilize programinstructions executable by a device (e.g., a computer, a processor, orother device) to perform, or control performance of, the processes ormethods.

In this respect, various inventive concepts may be embodied as acomputer readable storage medium (or multiple computer readable storagemedia) (e.g., a computer memory, one or more floppy discs, compactdiscs, optical discs, magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other tangible computer storage medium) encoded with one ormore programs that, when executed on one or more computers or otherprocessors, perform methods that implement one or more of the variousembodiments described above.

The computer readable medium or media may be transportable, such thatthe program or programs stored thereon may be loaded onto one or moredifferent computers or other processors to implement various ones of theaspects described above. In some embodiments, computer readable mediamay be non-transitory media.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that may be employed to program a computer or otherprocessor to implement various aspects as described above. Additionally,it should be appreciated that according to one aspect, one or morecomputer programs that when executed perform methods of the presentapplication need not reside on a single computer or processor, but maybe distributed in a modular fashion among a number of differentcomputers or processors to implement various aspects of the presentapplication.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that performs particular tasks or implement particularabstract data types. Typically, the functionality of the program modulesmay be combined or distributed as desired in various embodiments.

Also, data structures may be stored in computer-readable media in anysuitable form. For simplicity of illustration, data structures may beshown to have fields that are related through location in the datastructure. Such relationships may likewise be achieved by assigningstorage for the fields with locations in a computer-readable medium thatconvey relationship between the fields. However, any suitable mechanismmay be used to establish a relationship between information in fields ofa data structure, including through the use of pointers, tags or othermechanisms that establish relationship between data elements.

Also, as described, some aspects may be embodied as one or more methods.The acts performed as part of the method may be ordered in any suitableway. Accordingly, embodiments may be constructed in which acts areperformed in an order different than illustrated, which may includeperforming some acts simultaneously, even though shown as sequentialacts in illustrative embodiments.

The present disclosure should therefore not be considered limited to theparticular embodiments described above. Various modifications,equivalent processes, as well as numerous structures to which thepresent disclosure may be applicable, will be readily apparent to thoseskilled in the art to which the present disclosure is directed uponreview of the present disclosure.

Detailed embodiments of the present disclosure are disclosed herein;however, it is to be understood that the disclosed embodiments aremerely exemplary of the disclosure, which may be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present disclosure in virtually anyappropriately detailed structure.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “another,” as used herein, is defined as at least a secondor more. The terms “including” and/or “having”, as used herein, aredefined as comprising (i.e., open transition).

While only a few embodiments of the present disclosure have been shownand described, it will be obvious to those skilled in the art that manychanges and modifications may be made thereunto without departing fromthe spirit and scope of the present disclosure as described in thefollowing claims. All patent applications and patents, both foreign anddomestic, and all other publications referenced herein are incorporatedherein in their entireties to the full extent permitted by law.

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software, program codes,and/or instructions on a processor. The present disclosure may beimplemented as a method on the machine, as a system or apparatus as partof or in relation to the machine, or as a computer program productembodied in a computer readable medium executing on one or more of themachines. In embodiments, the processor may be part of a server, cloudserver, client, network infrastructure, mobile computing platform,stationary computing platform, or other computing platform. A processormay be any kind of computational or processing device capable ofexecuting program instructions, codes, binary instructions and the like.The processor may be or may include a signal processor, digitalprocessor, embedded processor, microprocessor or any variant such as aco-processor (math co-processor, graphic co-processor, communicationco-processor and the like) and the like that may directly or indirectlyfacilitate execution of program code or program instructions storedthereon. In addition, the processor may enable execution of multipleprograms, threads, and codes. The threads may be executed simultaneouslyto enhance the performance of the processor and to facilitatesimultaneous operations of the application. By way of implementation,methods, program codes, program instructions and the like describedherein may be implemented in one or more thread. The thread may spawnother threads that may have assigned priorities associated with them;the processor may execute these threads based on priority or any otherorder based on instructions provided in the program code. The processor,or any machine utilizing one, may include non-transitory memory thatstores methods, codes, instructions and programs as described herein andelsewhere. The processor may access a non-transitory storage mediumthrough an interface that may store methods, codes, and instructions asdescribed herein and elsewhere. The storage medium associated with theprocessor for storing methods, programs, codes, program instructions orother type of instructions capable of being executed by the computing orprocessing device may include but may not be limited to one or more of aCD-ROM, DVD, memory, hard disk, flash drive, RAM, ROM, cache and thelike.

A processor may include one or more cores that may enhance speed andperformance of a multiprocessor. In embodiments, the process may be adual core processor, quad core processors, other chip-levelmultiprocessor and the like that combine two or more independent cores(called a die).

The methods and systems described herein may be deployed in part or inwhole through a machine that executes computer software on a server,client, firewall, gateway, hub, router, or other such computer and/ornetworking hardware. The software program may be associated with aserver that may include a file server, print server, domain server,internet server, intranet server, cloud server, and other variants suchas secondary server, host server, distributed server and the like. Theserver may include one or more of memories, processors, computerreadable media, storage media, ports (physical and virtual),communication devices, and interfaces capable of accessing otherservers, clients, machines, and devices through a wired or a wirelessmedium, and the like. The methods, programs, or codes as describedherein and elsewhere may be executed by the server. In addition, otherdevices required for execution of methods as described in thisapplication may be considered as a part of the infrastructure associatedwith the server.

The server may provide an interface to other devices including, withoutlimitation, clients, other servers, printers, database servers, printservers, file servers, communication servers, distributed servers,social networks, and the like. Additionally, this coupling and/orconnection may facilitate remote execution of program across thenetwork. The networking of some or all of these devices may facilitateparallel processing of a program or method at one or more locationwithout deviating from the scope of the disclosure. In addition, any ofthe devices attached to the server through an interface may include atleast one storage medium capable of storing methods, programs, codeand/or instructions. A central repository may provide programinstructions to be executed on different devices. In thisimplementation, the remote repository may act as a storage medium forprogram code, instructions, and programs.

The software program may be associated with a client that may include afile client, print client, domain client, internet client, intranetclient and other variants such as secondary client, host client,distributed client and the like. The client may include one or more ofmemories, processors, computer readable media, storage media, ports(physical and virtual), communication devices, and interfaces capable ofaccessing other clients, servers, machines, and devices through a wiredor a wireless medium, and the like. The methods, programs, or codes asdescribed herein and elsewhere may be executed by the client. Inaddition, other devices required for execution of methods as describedin this application may be considered as a part of the infrastructureassociated with the client.

The client may provide an interface to other devices including, withoutlimitation, servers, other clients, printers, database servers, printservers, file servers, communication servers, distributed servers andthe like. Additionally, this coupling and/or connection may facilitateremote execution of program across the network. The networking of someor all of these devices may facilitate parallel processing of a programor method at one or more location without deviating from the scope ofthe disclosure. In addition, any of the devices attached to the clientthrough an interface may include at least one storage medium capable ofstoring methods, programs, applications, code and/or instructions. Acentral repository may provide program instructions to be executed ondifferent devices. In this implementation, the remote repository may actas a storage medium for program code, instructions, and programs.

The methods and systems described herein may be deployed in part or inwhole through network infrastructures. The network infrastructure mayinclude elements such as computing devices, servers, routers, hubs,firewalls, clients, personal computers, communication devices, routingdevices and other active and passive devices, modules and/or componentsas known in the art. The computing and/or non-computing device(s)associated with the network infrastructure may include, apart from othercomponents, a storage medium such as flash memory, buffer, stack, RAM,ROM and the like. The processes, methods, program codes, instructionsdescribed herein and elsewhere may be executed by one or more of thenetwork infrastructural elements. The methods and systems describedherein may be adapted for use with any kind of private, community, orhybrid cloud computing network or cloud computing environment, includingthose which involve features of software as a service (SaaS), platformas a service (PaaS), and/or infrastructure as a service (IaaS).

The methods, program codes, and instructions described herein andelsewhere may be implemented on or through mobile devices. The mobiledevices may include navigation devices, cell phones, mobile phones,mobile personal digital assistants, laptops, palmtops, netbooks, pagers,electronic books readers, music players and the like. These devices mayinclude, apart from other components, a storage medium such as a flashmemory, buffer, RAM, ROM and one or more computing devices. Thecomputing devices associated with mobile devices may be enabled toexecute program codes, methods, and instructions stored thereon.Alternatively, the mobile devices may be configured to executeinstructions in collaboration with other devices. The mobile devices maycommunicate with base stations interfaced with servers and configured toexecute program codes. The mobile devices may communicate on apeer-to-peer network, mesh network, or other communications network. Theprogram code may be stored on the storage medium associated with theserver and executed by a computing device embedded within the server.The base station may include a computing device and a storage medium.The storage device may store program codes and instructions executed bythe computing devices associated with the base station.

The computer software, program codes, and/or instructions may be storedand/or accessed on machine readable media that may include: computercomponents, devices, and recording media that retain digital data usedfor computing for some interval of time; semiconductor storage known asrandom access memory (RAM); mass storage typically for more permanentstorage, such as optical discs, forms of magnetic storage like harddisks, tapes, drums, cards and other types; processor registers, cachememory, volatile memory, non-volatile memory; optical storage such asCD, DVD; removable media such as flash memory (e.g. USB sticks or keys),floppy disks, magnetic tape, paper tape, punch cards, standalone RAMdisks, Zip drives, removable mass storage, off-line, and the like; othercomputer memory such as dynamic memory, static memory, read/writestorage, mutable storage, read only, random access, sequential access,location addressable, file addressable, content addressable, networkattached storage, storage area network, bar codes, magnetic ink, and thelike.

The methods and systems described herein may transform physical and/orintangible items from one state to another. The methods and systemsdescribed herein may also transform data representing physical and/orintangible items from one state to another.

The elements described and depicted herein, including in flowcharts andblock diagrams throughout the figures, imply logical boundaries betweenthe elements. However, according to software or hardware engineeringpractices, the depicted elements and the functions thereof may beimplemented on machines through computer executable media having aprocessor capable of executing program instructions stored thereon as amonolithic software structure, as standalone software modules, or asmodules that employ external routines, code, services, and so forth, orany combination of these, and all such implementations may be within thescope of the present disclosure. Examples of such machines may include,but may not be limited to, personal digital assistants, laptops,personal computers, mobile phones, other handheld computing devices,medical equipment, wired or wireless communication devices, transducers,chips, calculators, satellites, tablet PCs, electronic books, gadgets,electronic devices, devices having artificial intelligence, computingdevices, networking equipment, servers, routers and the like.Furthermore, the elements depicted in the flowchart and block diagramsor any other logical component may be implemented on a machine capableof executing program instructions. Thus, while the foregoing drawingsand descriptions set forth functional aspects of the disclosed systems,no particular arrangement of software for implementing these functionalaspects should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. Similarly, it will beappreciated that the various steps identified and described above may bevaried, and that the order of steps may be adapted to particularapplications of the techniques disclosed herein. All such variations andmodifications are intended to fall within the scope of this disclosure.As such, the depiction and/or description of an order for various stepsshould not be understood to require a particular order of execution forthose steps, unless required by a particular application, or explicitlystated or otherwise clear from the context.

The methods and/or processes described above, and steps associatedtherewith, may be realized in hardware, software or any combination ofhardware and software suitable for a particular application. Thehardware may include a general-purpose computer and/or dedicatedcomputing device or specific computing device or particular aspect orcomponent of a specific computing device. The processes may be realizedin one or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors or otherprogrammable device, along with internal and/or external memory. Theprocesses may also, or instead, be embodied in an application specificintegrated circuit, a programmable gate array, programmable array logic,or any other device or combination of devices that may be configured toprocess electronic signals. It will further be appreciated that one ormore of the processes may be realized as a computer executable codecapable of being executed on a machine-readable medium.

The computer executable code may be created using a structuredprogramming language such as C, an object oriented programming languagesuch as C++, or any other high-level or low-level programming language(including assembly languages, hardware description languages, anddatabase programming languages and technologies) that may be stored,compiled or interpreted to run on one of the above devices, as well asheterogeneous combinations of processors, processor architectures, orcombinations of different hardware and software, or any other machinecapable of executing program instructions.

Thus, in one aspect, methods described above and combinations thereofmay be embodied in computer executable code that, when executing on oneor more computing devices, performs the steps thereof. In anotheraspect, the methods may be embodied in systems that perform the stepsthereof, and may be distributed across devices in a number of ways, orall of the functionality may be integrated into a dedicated, standalonedevice or other hardware. In another aspect, the means for performingthe steps associated with the processes described above may include anyof the hardware and/or software described above. All such permutationsand combinations are intended to fall within the scope of the presentdisclosure.

While the disclosure has been disclosed in connection with the preferredembodiments shown and described in detail, various modifications andimprovements thereon will become readily apparent to those skilled inthe art. Accordingly, the spirit and scope of the present disclosure isnot to be limited by the foregoing examples, but is to be understood inthe broadest sense allowable by law.

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the disclosure (especially in the context of thefollowing claims) is to be construed to cover both the singular and theplural, unless otherwise indicated herein or clearly contradicted bycontext. The terms “comprising,” “having,” “including,” and “containing”are to be construed as open-ended terms (i.e., meaning “including, butnot limited to,”) unless otherwise noted. Recitation of ranges of valuesherein are merely intended to serve as a shorthand method of referringindividually to each separate value falling within the range, unlessotherwise indicated herein, and each separate value is incorporated intothe specification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the disclosureand does not pose a limitation on the scope of the disclosure unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe disclosure.

While the foregoing written description enables one of ordinary skill tomake and use what is considered presently to be the best mode thereof,those of ordinary skill will understand and appreciate the existence ofvariations, combinations, and equivalents of the specific embodiment,method, and examples herein. The disclosure should therefore not belimited by the above described embodiment, method, and examples, but byall embodiments and methods within the scope and spirit of thedisclosure.

Any element in a claim that does not explicitly state “means for”performing a specified function, or “step for” performing a specifiedfunction, is not to be interpreted as a “means” or “step” clause asspecified in 35 U.S.C. § 112(f). In particular, any use of “step of” inthe claims is not intended to invoke the provision of 35 U.S.C. §112(f).

Persons of ordinary skill in the art may appreciate that numerous designconfigurations may be possible to enjoy the functional benefits of theinventive systems. Thus, given the wide variety of configurations andarrangements of embodiments of the present disclosure, the scope of thedisclosure is reflected by the breadth of the claims below rather thannarrowed by the embodiments described above.

What is claimed is:
 1. A system for generating a cluster of correlatedcontent from a primary online content object, comprising: an automatedcrawler for crawling the primary online content object and storing a setof results from the crawling in a data storage facility; a parser forparsing the stored set of results from the crawling to generate aplurality of key phrases and to generate a content corpus from theprimary online content object; a plurality of models for processing atleast one of the plurality of key phrases and the content corpus, theplurality of models comprising at least two of a word2vec model, adoc2vec model, a latent semantic analysis (LSA) extraction model, and akey phrase logistic regression model, wherein the processing results ina plurality of content clusters representing topics within the primaryonline content object; a content cluster data store for storing theplurality of content clusters; and a suggestion generator forgenerating, using output from at least one of the plurality of models, asuggested topic that is similar to at least one topic among theplurality of content clusters and for storing the suggested topic andinformation regarding a similarity of the suggested topic to at leastone content cluster in the content cluster data store.
 2. The system ofclaim 1, wherein the plurality of models comprises at least one of theword2vec model, the doc2vec model, the latent semantic analysisextraction model, the latent semantic indexing model, the principlecomponent analysis model, and the key phrase logistic regression model.3. The system of claim 1, wherein the parser uses a machine learningsystem to parse the crawled content.
 4. The system of claim 3, whereinthe machine learning system iteratively applies a set of weights toinput data, wherein the set of weights are adjusted based on a parameterof success, wherein the parameter of success is based on a success ofsuggested topics in an online presence of an enterprise.
 5. The systemof claim 4, wherein the machine learning system is provided with atraining data set that is created based on human analysis of the crawledcontent.
 6. The system of claim 1, wherein at least one of the pluralityof models uses a machine learning system to cluster content.
 7. Thesystem of claim 6, wherein the machine learning system iterativelyapplies a set of weights to input data, wherein the set of weights areadjusted based on a parameter of success, wherein the parameter ofsuccess is based on a success of suggested topics in an online presenceof an enterprise.
 8. The system of claim 7, wherein the machine learningsystem is provided with a training data set that is created based onhuman clustering of a set of content topics.
 9. The system of claim 1,wherein the suggestion generator uses a machine learning system tosuggest topics.
 10. The system of claim 9, wherein the machine learningsystem iteratively applies a set of weights to input data, wherein theset of weights are adjusted based on a parameter of success, wherein theparameter of success is based on a success of suggested topics in anonline presence of an enterprise.
 11. The system of claim 10, whereinthe machine learning system is provided with a training data set that iscreated based on human creation of a set of suggested topics.
 12. Thesystem of claim 1, further comprising, an application for developing astrategy for development of online presence content, the applicationaccessing the content cluster data store and having a set of tools forexploring and selecting suggested topics for online presence contentgeneration.
 13. The system of claim 12, wherein the application providesa list of topics that are of highest semantic relevance for anenterprise based on the parsing of the primary online content object.14. The system of claim 13, further comprising, a user interface of theapplication for presenting the suggested topic, wherein the suggestedtopic is presented with an indicator of the similarity of the suggestedtopic to a content cluster topic as calculated by at least one of theplurality of models.
 15. The system of claim 13, further comprising auser interface of the application for presenting the suggested topic,wherein the user interface facilitates generation of content related tothe suggested topic.
 16. The system of claim 15, wherein the userinterface includes at least one of key words and key phrases thatrepresent the suggested topic.
 17. The system of claim 16, wherein theat least one of key words and key phrases are used to prompt a user withcontent for generation of online presence content.
 18. The system ofclaim 17, wherein the online presence content is at least one of websitecontent, mobile application content, a social media post, a customerchat, a frequently asked question item, a product description, a servicedescription and a marketing message.
 19. The system of claim 18, whereinthe user interface for generation of content includes a plurality ofsuggested topics, each associated with an indicator of the similarity ofa given suggested topic to a content cluster topic as calculated by atleast one of the plurality of models.
 20. The system of claim 1, whereinthe data storage facility is a cloud-based storage facility.
 21. Thesystem of claim 1, wherein the data storage facility is a distributeddata storage facility.
 22. The system of claim 1, wherein the primaryonline content object is a web page of an enterprise.
 23. The system ofclaim 1, wherein the primary online content object is a social mediapage of an enterprise.
 24. The system of claim 1, further comprising, anapplication for developing a strategy for development of online presencecontent, the application accessing the content cluster data store andhaving a set of tools for exploring and selecting suggested topics foronline presence content generation, wherein the application furtheraccesses the content of a customer relationship management system. 25.The system of claim 24, wherein the application comprises a userinterface for developing content regarding the suggested topic forpresentation in a communication to a customer, wherein selection of thesuggested topic for presentation to the customer is based at least inpart on a semantic relationship between the suggested topic asdetermined by at least one of the plurality of models and at least onedata record relating to the customer stored in the customer relationshipmanagement system.